Android 3.0 and later platform versions are optimized to support
multiprocessor architectures. This document introduces issues that
can arise when writing code for symmetric multiprocessor systems in C, C++, and the Java
programming language (hereafter referred to simply as “Java” for the sake of
brevity). It's intended as a primer for Android app developers, not as a complete
discussion on the subject. The focus is on the ARM CPU architecture.

If you’re in a hurry, you can skip the Theory section
and go directly to Practice for best practices, but this
is not recommended.

Introduction

SMP is an acronym for “Symmetric Multi-Processor”. It describes a design in
which two or more identical CPU cores share access to main memory. Until
a few years ago, all Android devices were UP (Uni-Processor).

Most — if not all — Android devices do have multiple CPUs, but generally one
of them is used to run applications while others manage various bits of device
hardware (for example, the radio). The CPUs may have different architectures, and the
programs running on them can’t use main memory to communicate with each
other.

Most Android devices sold today are built around SMP designs,
making things a bit more complicated for software developers. The sorts of race
conditions you might encounter in a multi-threaded program are much worse on SMP
when two or more of your threads are running simultaneously on different cores.
What’s more, SMP on ARM is more challenging to work with than SMP on x86. Code
that has been thoroughly tested on x86 may break badly on ARM.

The rest of this document will explain why, and tell you what you need to do
to ensure that your code behaves correctly.

Theory

This is a high-speed, glossy overview of a complex subject. Some areas will
be incomplete, but none of it should be misleading or wrong.

See Further reading at the end of the document for
pointers to more thorough treatments of the subject.

Memory consistency models

Memory consistency models, or often just “memory models”, describe the
guarantees the hardware architecture makes about memory accesses. For example,
if you write a value to address A, and then write a value to address B, the
model might guarantee that every CPU core sees those writes happen in that
order.

The model most programmers are accustomed to is sequential
consistency, which is described like this (Adve &
Gharachorloo):

All memory operations appear to execute one at a time

All operations on a single processor appear to execute in the order described
by that processor's program.

If you look at a bit of code and see that it does some reads and writes from
memory, on a sequentially-consistent CPU architecture you know that the code
will do those reads and writes in the expected order. It’s possible that the
CPU is actually reordering instructions and delaying reads and writes, but there
is no way for code running on the device to tell that the CPU is doing anything
other than execute instructions in a straightforward manner. (We’re ignoring
memory-mapped device driver I/O for the moment.)

To illustrate these points it’s useful to consider small snippets of code,
commonly referred to as litmus tests. These are assumed to execute in
program order, that is, the order in which the instructions appear here is
the order in which the CPU will execute them. We don’t want to consider
instruction reordering performed by compilers just yet.

Here’s a simple example, with code running on two threads:

Thread 1

Thread 2

A = 3
B = 5

reg0 = B
reg1 = A

In this and all future litmus examples, memory locations are represented by
capital letters (A, B, C) and CPU registers start with “reg”. All memory is
initially zero. Instructions are executed from top to bottom. Here, thread 1
stores the value 3 at location A, and then the value 5 at location B. Thread 2
loads the value from location B into reg0, and then loads the value from
location A into reg1. (Note that we’re writing in one order and reading in
another.)

Thread 1 and thread 2 are assumed to execute on different CPU cores. You
should always make this assumption when thinking about
multi-threaded code.

Sequential consistency guarantees that, after both threads have finished
executing, the registers will be in one of the following states:

Registers

States

reg0=5, reg1=3

possible (thread 1 ran first)

reg0=0, reg1=0

possible (thread 2 ran first)

reg0=0, reg1=3

possible (concurrent execution)

reg0=5, reg1=0

never

To get into a situation where we see B=5 before we see the store to A, either
the reads or the writes would have to happen out of order. On a
sequentially-consistent machine, that can’t happen.

Most uni-processors, including x86 and ARM, are sequentially consistent.
Most SMP systems, including x86 and ARM, are not.

Processor consistency

x86 SMP provides processor consistency, which is slightly weaker than
sequential. While the architecture guarantees that loads are not reordered with
respect to other loads, and stores are not reordered with respect to other
stores, it does not guarantee that a store followed by a load will be observed
in the expected order.

Consider the following example, which is a piece of Dekker’s Algorithm for
mutual exclusion:

Thread 1

Thread 2

A = true
reg1 = B
if (reg1 == false)critical-stuff

B = true
reg2 = A
if (reg2 == false)critical-stuff

The idea is that thread 1 uses A to indicate that it’s busy, and thread 2
uses B. Thread 1 sets A and then checks to see if B is set; if not, it can
safely assume that it has exclusive access to the critical section. Thread 2
does something similar. (If a thread discovers that both A and B are set, a
turn-taking algorithm is used to ensure fairness.)

On a sequentially-consistent machine, this works correctly. On x86 and ARM
SMP, the store to A and the load from B in thread 1 can be “observed” in a
different order by thread 2. If that happened, we could actually appear to
execute this sequence (where blank lines have been inserted to highlight the
apparent order of operations):

Thread 1

Thread 2

reg1 = B

A = true
if (reg1 == false)critical-stuff

B = true
reg2 = A

if (reg2 == false)critical-stuff

This results in both reg1 and reg2 set to “false”, allowing the threads to
execute code in the critical section simultaneously. To understand how this can
happen, it’s useful to know a little about CPU caches.

CPU cache behavior

This is a substantial topic in and of itself. An extremely brief overview
follows. (The motivation for this material is to provide some basis for
understanding why SMP systems behave as they do.)

Modern CPUs have one or more caches between the processor and main memory.
These are labeled L1, L2, and so on, with the higher numbers being successively
“farther” from the CPU. Cache memory adds size and cost to the hardware, and
increases power consumption, so the ARM CPUs used in Android devices typically
have small L1 caches and little or no L2/L3.

Loading or storing a value into the L1 cache is very fast. Doing the same to
main memory can be 10-100x slower. The CPU will therefore try to operate out of
the cache as much as possible. The write policy of a cache determines when data
written to it is forwarded to main memory. A write-through cache will initiate
a write to memory immediately, while a write-back cache will wait until it runs
out of space and has to evict some entries. In either case, the CPU will
continue executing instructions past the one that did the store, possibly
executing dozens of them before the write is visible in main memory. (While the
write-through cache has a policy of immediately forwarding the data to main
memory, it only initiates the write. It does not have to wait
for it to finish.)

The cache behavior becomes relevant to this discussion when each CPU core has
its own private cache. In a simple model, the caches have no way to interact
with each other directly. The values held by core #1’s cache are not shared
with or visible to core #2’s cache except as loads or stores from main memory.
The long latencies on memory accesses would make inter-thread interactions
sluggish, so it’s useful to define a way for the caches to share data. This
sharing is called cache coherency, and the coherency rules are defined
by the CPU architecture’s cache consistency model.

With that in mind, let’s return to the Dekker example. When core 1 executes
“A = 1”, the value gets stored in core 1’s cache. When core 2 executes “if (A
== 0)”, it might read from main memory or it might read from core 2’s cache;
either way it won’t see the store performed by core 1. (“A” could be in core
2’s cache because of a previous load from “A”.)

For the memory consistency model to be sequentially consistent, core 1 would
have to wait for all other cores to be aware of “A = 1” before it could execute
“if (B == 0)” (either through strict cache coherency rules, or by disabling the
caches entirely so everything operates out of main memory). This would impose a
performance penalty on every store operation. Relaxing the rules for the
ordering of stores followed by loads improves performance but imposes a burden
on software developers.

The other guarantees made by the processor consistency model are less
expensive to make. For example, to ensure that memory writes are not observed
out of order, it just needs to ensure that the stores are published to other
cores in the same order that they were issued. It doesn’t need to wait for
store #1 to finish being published before it can start on store
#2, it just needs to ensure that it doesn’t finish publishing #2 before it
finishes publishing #1. This avoids a performance bubble.

Relaxing the guarantees even further can provide additional opportunities for
CPU optimization, but creates more opportunities for code to behave in ways the
programmer didn’t expect.

One additional note: CPU caches don’t operate on individual bytes. Data is
read or written as cache lines; for many ARM CPUs these are 32 bytes. If you
read data from a location in main memory, you will also be reading some adjacent
values. Writing data will cause the cache line to be read from memory and
updated. As a result, you can cause a value to be loaded into cache as a
side-effect of reading or writing something nearby, adding to the general aura
of mystery.

Observability

Before going further, it’s useful to define in a more rigorous fashion what
is meant by “observing” a load or store. Suppose core 1 executes “A = 1”. The
store is initiated when the CPU executes the instruction. At some
point later, possibly through cache coherence activity, the store is
observed by core 2. In a write-through cache it doesn’t really
complete until the store arrives in main memory, but the memory
consistency model doesn’t dictate when something completes, just when it can be
observed.

(In a kernel device driver that accesses memory-mapped I/O locations, it may
be very important to know when things actually complete. We’re not going to go
into that here.)

Observability may be defined as follows:

"A write to a location in memory is said to be observed by an observer Pn
when a subsequent read of the location by Pn would return the value written by
the write."

A less formal way to describe it (where “you” and “I” are CPU cores) would be:

I have observed your write when I can read what you wrote

I have observed your read when I can no longer affect the value you read

The notion of observing a write is intuitive; observing a read is a bit less
so (don’t worry, it grows on you).

With this in mind, we’re ready to talk about ARM.

ARM's weak ordering

ARM SMP provides weak memory consistency guarantees. It does not guarantee that
loads or stores are ordered with respect to each other.

Thread 1

Thread 2

A = 41
B = 1 // “A is ready”

loop_until (B == 1)
reg = A

Recall that all addresses are initially zero. The “loop_until” instruction
reads B repeatedly, looping until we read 1 from B. The idea here is that
thread 2 is waiting for thread 1 to update A. Thread 1 sets A, and then sets B
to 1 to indicate data availability.

On x86 SMP, this is guaranteed to work. Thread 2 will observe the stores
made by thread 1 in program order, and thread 1 will observe thread 2’s loads in
program order.

On ARM SMP, the loads and stores can be observed in any order. It is
possible, after all the code has executed, for reg to hold 0. It’s also
possible for it to hold 41. Unless you explicitly define the ordering, you
don’t know how this will come out.

(For those with experience on other systems, ARM’s memory model is equivalent
to PowerPC in most respects.)

Data memory barriers

Memory barriers provide a way for your code to tell the CPU that memory
access ordering matters. ARM/x86 uniprocessors offer sequential consistency,
and thus have no need for them. (The barrier instructions can be executed but
aren’t useful; in at least one case they’re hideously expensive, motivating
separate builds for SMP targets.)

There are four basic situations to consider:

store followed by another store

load followed by another load

load followed by store

store followed by load

Store/store and load/load

Recall our earlier example:

Thread 1

Thread 2

A = 41
B = 1 // “A is ready”

loop_until (B == 1)
reg = A

Thread 1 needs to ensure that the store to A happens before the store to B.
This is a “store/store” situation. Similarly, thread 2 needs to ensure that the
load of B happens before the load of A; this is a load/load situation. As
mentioned earlier, the loads and stores can be observed in any order.

Going back to the cache discussion, assume A and B are on separate cache
lines, with minimal cache coherency. If the store to A stays local but the
store to B is published, core 2 will see B=1 but won’t see the update to A. On
the other side, assume we read A earlier, or it lives on the same cache line as
something else we recently read. Core 2 spins until it sees the update to B,
then loads A from its local cache, where the value is still zero.

We can fix it like this:

Thread 1

Thread 2

A = 41store/store barrier
B = 1 // “A is ready”

loop_until (B == 1)load/load barrier
reg = A

The store/store barrier guarantees that all observers will
observe the write to A before they observe the write to B. It makes no
guarantees about the ordering of loads in thread 1, but we don’t have any of
those, so that’s okay. The load/load barrier in thread 2 makes a similar
guarantee for the loads there.

Since the store/store barrier guarantees that thread 2 observes the stores in
program order, why do we need the load/load barrier in thread 2? Because we
also need to guarantee that thread 1 observes the loads in program order.

The store/store barrier could work by flushing all
dirty entries out of the local cache, ensuring that other cores see them before
they see any future stores. The load/load barrier could purge the local cache
completely and wait for any “in-flight” loads to finish, ensuring that future
loads are observed after previous loads. What the CPU actually does doesn’t
matter, so long as the appropriate guarantees are kept. If we use a barrier in
core 1 but not in core 2, core 2 could still be reading A from its local
cache.

Because the architectures have different memory models, these barriers are
required on ARM SMP but not x86 SMP.

Load/store and store/load

The Dekker’s Algorithm fragment shown earlier illustrated the need for a
store/load barrier. Here’s an example where a load/store barrier is
required:

Thread 1

Thread 2

reg = A
B = 1 // “I have latched A”

loop_until (B == 1)
A = 41 // update A

Thread 2 could observe thread 1’s store of B=1 before it observe’s thread 1’s
load from A, and as a result store A=41 before thread 1 has a chance to read A.
Inserting a load/store barrier in each thread solves the problem:

Thread 1

Thread 2

reg = Aload/store barrier
B = 1 // “I have latched A”

loop_until (B == 1)load/store barrier
A = 41 // update A

A store to local cache may be observed before a load from main memory,
because accesses to main memory are so much slower. In this case, assume core
1’s cache has the cache line for B but not A. The load from A is initiated, and
while that’s in progress execution continues. The store to B happens in local
cache, and by some means becomes available to core 2 while the load from A is
still in progress. Thread 2 is able to exit the loop before it has observed
thread 1’s load from A.

A thornier question is: do we need a barrier in thread 2? If the CPU doesn’t
perform speculative writes, and doesn’t execute instructions out of order, can
thread 2 store to A before thread 1’s read if thread 1 guarantees the load/store
ordering? (Answer: no.) What if there’s a third core watching A and B?
(Answer: now you need one, or you could observe B==0 / A==41 on the third core.)
It’s safest to insert barriers in both places and not worry about the
details.

As mentioned earlier, store/load barriers are the only kind required on x86
SMP.

Barrier instructions

Different CPUs provide different flavors of barrier instruction. For
example:

Sparc V8 has a “membar” instruction that takes a 4-element bit vector. The
four categories of barrier can be specified individually.

x86 has a variety of options; “mfence” (introduced with SSE2) provides a
full barrier.

ARMv7 has “dmb st” (store/store) and “dmb sy” (full).

“Full barrier” means all four categories are included.

It is important to recognize that the only thing guaranteed by barrier
instructions is ordering. Do not treat them as cache coherency “sync points” or
synchronous “flush” instructions. The ARM “dmb” instruction has no direct
effect on other cores. This is important to understand when trying to figure
out where barrier instructions need to be issued.

Address dependencies and causal consistency

(This is a slightly more advanced topic and can be skipped.)

The ARM CPU provides one special case where a load/load barrier can be
avoided. Consider the following example from earlier, modified slightly:

Thread 1

Thread 2

[A+8] = 41store/store barrier
B = 1 // “A is ready”

loop:
reg0 = B
if (reg0 == 0) goto loop
reg1 = 8
reg2 = [A + reg1]

This introduces a new notation. If “A” refers to a memory address, “A+n”
refers to a memory address offset by 8 bytes from A. If A is the base address
of an object or array, [A+8] could be a field in the object or an element in the
array.

The “loop_until” seen in previous examples has been expanded to show the load
of B into reg0. reg1 is assigned the numeric value 8, and reg2 is loaded from
the address [A+reg1] (the same location that thread 1 is accessing).

This will not behave correctly because the load from B could be observed
after the load from [A+reg1]. We can fix this with a load/load barrier after
the loop, but on ARM we can also just do this:

What we’ve done here is change the assignment of reg1 from a constant (8) to
a value that depends on what we loaded from B. In this case, we do a bitwise
AND of the value with 0, which yields zero, which means reg1 still has the value
8. However, the ARM CPU believes that the load from [A+reg1] depends upon the
load from B, and will ensure that the two are observed in program order.

This is called an address dependency. Address dependencies exist
when the value returned by a load is used to compute the address of a subsequent
load or store. It can let you avoid the need for an explicit barrier in certain
situations.

The loads from r0 and r3 may be observed out of order, even though the load
from r3 will not execute at all if [r0] doesn’t hold 55. Inserting AND r1, r1,
#0 and replacing the last instruction with LDRNE r2, [r3, r1] would ensure
proper ordering without an explicit barrier. (This is a prime example of why
you can’t think about consistency issues in terms of instruction execution.
Always think in terms of memory accesses.)

While we’re hip-deep, it’s worth noting that ARM does not provide causal
consistency:

Here, thread 1 sets A, signaling thread 2. Thread 2 sees that and sets B to
signal thread 3. Thread 3 sees it and loads from A, using an address dependency
to ensure that the load of B and the load of A are observed in program
order.

It’s possible for reg2 to hold zero at the end of this. The fact that a
store in thread 1 causes something to happen in thread 2 which causes something
to happen in thread 3 does not mean that thread 3 will observe the stores in
that order. (Inserting a load/store barrier in thread 2 fixes this.)

Memory barrier summary

Barriers come in different flavors for different situations. While there can
be performance advantages to using exactly the right barrier type, there are
code maintenance risks in doing so — unless the person updating the code
fully understands it, they might introduce the wrong type of operation and cause
a mysterious breakage. Because of this, and because ARM doesn’t provide a wide
variety of barrier choices, many atomic primitives use full
barrier instructions when a barrier is required.

The key thing to remember about barriers is that they define ordering. Don’t
think of them as a “flush” call that causes a series of actions to happen.
Instead, think of them as a dividing line in time for operations on the current
CPU core.

Atomic operations

Atomic operations guarantee that an operation that requires a series of steps
always behaves as if it were a single operation. For example, consider a
non-atomic increment (“++A”) executed on the same variable by two threads
simultaneously:

Thread 1

Thread 2

reg = A
reg = reg + 1
A = reg

reg = A
reg = reg + 1
A = reg

If the threads execute concurrently from top to bottom, both threads will
load 0 from A, increment it to 1, and store it back, leaving a final result of
1. If we used an atomic increment operation, you would be guaranteed that the
final result will be 2.

Atomic essentials

The most fundamental operations — loading and storing 32-bit values
— are inherently atomic on ARM so long as the data is aligned on a 32-bit
boundary. For example:

Thread 1

Thread 2

reg = 0x00000000
A = reg

reg = 0xffffffff
A = reg

The CPU guarantees that A will hold 0x00000000 or 0xffffffff. It will never
hold 0x0000ffff or any other partial “mix” of bytes.

The atomicity guarantee is lost if the data isn’t aligned. Misaligned data
could straddle a cache line, so other cores could see the halves update
independently. Consequently, the ARMv7 documentation declares that it provides
“single-copy atomicity” for all byte accesses, halfword accesses to
halfword-aligned locations, and word accesses to word-aligned locations.
Doubleword (64-bit) accesses are not atomic, unless the
location is doubleword-aligned and special load/store instructions are used.
This behavior is important to understand when multiple threads are performing
unsynchronized updates to packed structures or arrays of primitive types.

There is no need for 32-bit “atomic read” or “atomic write” functions on ARM
or x86. Where one is provided for completeness, it just does a trivial load or
store.

Operations that perform more complex actions on data in memory are
collectively known as read-modify-write (RMW) instructions, because
they load data, modify it in some way, and write it back. CPUs vary widely in
how these are implemented. ARM uses a technique called “Load Linked / Store
Conditional”, or LL/SC.

A linked or locked load reads the data from memory as
usual, but also establishes a reservation, tagging the physical memory address.
The reservation is cleared when another core tries to write to that address. To
perform an LL/SC, the data is read with a reservation, modified, and then a
conditional store instruction is used to try to write the data back. If the
reservation is still in place, the store succeeds; if not, the store will fail.
Atomic functions based on LL/SC usually loop, retrying the entire
read-modify-write sequence until it completes without interruption.

It’s worth noting that the read-modify-write operations would not work
correctly if they operated on stale data. If two cores perform an atomic
increment on the same address, and one of them is not able to see what the other
did because each core is reading and writing from local cache, the operation
won’t actually be atomic. The CPU’s cache coherency rules ensure that the
atomic RMW operations remain atomic in an SMP environment.

This should not be construed to mean that atomic RMW operations use a memory
barrier. On ARM, atomics have no memory barrier semantics. While a series of
atomic RMW operations on a single address will be observed in program order by
other cores, there are no guarantees when it comes to the ordering of atomic and
non-atomic operations.

It often makes sense to pair barriers and atomic operations together. The
next section describes this in more detail.

Atomic + barrier pairing

As usual, it’s useful to illuminate the discussion with an example. We’re
going to consider a basic mutual-exclusion primitive called a spin
lock. The idea is that a memory address (which we’ll call “lock”)
initially holds zero. When a thread wants to execute code in the critical
section, it sets the lock to 1, executes the critical code, and then changes it
back to zero when done. If another thread has already set the lock to 1, we sit
and spin until the lock changes back to zero.

To make this work we use an atomic RMW primitive called
compare-and-swap. The function takes three arguments: the memory
address, the expected current value, and the new value. If the value currently
in memory matches what we expect, it is replaced with the new value, and the old
value is returned. If the current value is not what we expect, we don’t change
anything. A minor variation on this is called compare-and-set; instead
of returning the old value it returns a boolean indicating whether the swap
succeeded. For our needs either will work, but compare-and-set is slightly
simpler for examples, so we use it and just refer to it as “CAS”.

The acquisition of the spin lock is written like this (using a C-like
language):

If no thread holds the lock, the lock value will be 0, and the CAS operation
will set it to 1 to indicate that we now have it. If another thread has it, the
lock value will be 1, and the CAS operation will fail because the expected
current value does not match the actual current value. We loop and retry.
(Note this loop is on top of whatever loop the LL/SC code might be doing inside
the atomic_cas function.)

On SMP, a spin lock is a useful way to guard a small critical section. If we
know that another thread is going to execute a handful of instructions and then
release the lock, we can just burn a few cycles while we wait our turn.
However, if the other thread happens to be executing on the same core, we’re
just wasting time because the other thread can’t make progress until the OS
schedules it again (either by migrating it to a different core or by preempting
us). A proper spin lock implementation would optimistically spin a few times
and then fall back on an OS primitive (such as a Linux futex) that allows the
current thread to sleep while waiting for the other thread to finish up. On a
uniprocessor you never want to spin at all. For the sake of brevity we’re
ignoring all this.

The memory barrier is necessary to ensure that other threads observe the
acquisition of the lock before they observe any loads or stores in the critical
section. Without that barrier, the memory accesses could be observed while the
lock is not held.

The full_memory_barrier call here actually does
two independent operations. First, it issues the CPU’s full
barrier instruction. Second, it tells the compiler that it is not allowed to
reorder code around the barrier. That way, we know that the
atomic_cas call will be executed before anything in the critical
section. Without this compiler reorder barrier, the compiler has a
great deal of freedom in how it generates code, and the order of instructions in
the compiled code might be much different from the order in the source code.

Of course, we also want to make sure that none of the memory accesses
performed in the critical section are observed after the lock is released. The
full version of the simple spin lock is:

We perform our second CPU/compiler memory barrier immediately
before we release the lock, so that loads and stores in the
critical section are observed before the release of the lock.

As mentioned earlier, the atomic_store operation is a simple
assignment on ARM and x86. Unlike the atomic RMW operations, we don’t guarantee
that other threads will see this value immediately. This isn’t a problem,
though, because we only need to keep the other threads out. The
other threads will stay out until they observe the store of 0. If it takes a
little while for them to observe it, the other threads will spin a little
longer, but we will still execute code correctly.

It’s convenient to combine the atomic operation and the barrier call into a
single function. It also provides other advantages, which will become clear
shortly.

Acquire and release

When acquiring the spinlock, we issue the atomic CAS and then the barrier.
When releasing the spinlock, we issue the barrier and then the atomic store.
This inspires a particular naming convention: operations followed by a barrier
are “acquiring” operations, while operations preceded by a barrier are
“releasing” operations. (It would be wise to install the spin lock example
firmly in mind, as the names are not otherwise intuitive.)

This is a little more succinct and easier to read, but the real motivation
for doing this lies in a couple of optimizations we can now perform.

First, consider atomic_release_store. We need to ensure that
the store of zero to the lock word is observed after any loads or stores in the
critical section above it. In other words, we need a load/store and store/store
barrier. In an earlier section we learned that these aren’t necessary on x86
SMP -- only store/load barriers are required. The implementation of
atomic_release_store on x86 is therefore just a compiler reorder
barrier followed by a simple store. No CPU barrier is required.

The second optimization mostly applies to the compiler (although some CPUs,
such as the Itanium, can take advantage of it as well). The basic principle is
that code can move across acquire and release barriers, but only in one
direction.

Suppose we have a mix of locally-visible and globally-visible memory
accesses, with some miscellaneous computation as well:

Here we see two completely independent sets of operations. The first set
operates on a thread-local data structure, so we’re not concerned about clashes
with other threads. The second set operates on a global data structure, which
must be protected with a lock.

A full compiler reorder barrier in the atomic ops will ensure that the
program order matches the source code order at the lock boundaries. However,
allowing the compiler to interleave instructions can improve performance. Loads
from memory can be slow, but the CPU can continue to execute instructions that
don’t require the result of that load while waiting for it to complete. The
code might execute more quickly if it were written like this instead:

We issue both loads, do some unrelated computation, and then execute the
instructions that make use of the loads. If the integer division takes less
time than one of the loads, we essentially get it for free, since it happens
during a period where the CPU would have stalled waiting for a load to
complete.

Note that all of the operations are now happening inside the
critical section. Since none of the “threadStruct” operations are visible
outside the current thread, nothing else can see them until we’re finished here,
so it doesn’t matter exactly when they happen.

In general, it is always safe to move operations into a
critical section, but never safe to move operations out of a
critical section. Put another way, you can migrate code “downward” across an
acquire barrier, and “upward” across a release barrier. If the atomic ops used
a full barrier, this sort of migration would not be possible.

Returning to an earlier point, we can state that on x86 all loads are
acquiring loads, and all stores are releasing stores. As a result:

Loads may not be reordered with respect to each other. You can’t take a
load and move it “upward” across another load’s acquire barrier.

Stores may not be reordered with respect to each other, because you can’t
move a store “downward” across another store’s release barrier.

A load followed by a store can’t be reordered, because neither instruction
will tolerate it.

A store followed by a load can be reordered, because each
instruction can move across the other in that direction.

Hence, you only need store/load barriers on x86 SMP.

Labeling atomic operations with “acquire” or “release” describes not only
whether the barrier is executed before or after the atomic operation, but also
how the compiler is allowed to reorder code.

Practice

Debugging memory consistency problems can be very difficult. If a missing
memory barrier causes some code to read stale data, you may not be able to
figure out why by examining memory dumps with a debugger. By the time you can
issue a debugger query, the CPU cores will have all observed the full set of
accesses, and the contents of memory and the CPU registers will appear to be in
an “impossible” state.

What not to do in C

Here we present some examples of incorrect code, along with simple ways to
fix them. Before we do that, we need to discuss the use of a basic language
feature.

C/C++ and "volatile"

When writing single-threaded code, declaring a variable “volatile” can be
very useful. The compiler will not omit or reorder accesses to volatile
locations. Combine that with the sequential consistency provided by the
hardware, and you’re guaranteed that the loads and stores will appear to happen
in the expected order.

However, accesses to volatile storage may be reordered with non-volatile
accesses, so you have to be careful in multi-threaded uniprocessor environments
(explicit compiler reorder barriers may be required). There are no atomicity
guarantees, and no memory barrier provisions, so “volatile” doesn’t help you at
all in multi-threaded SMP environments. The C and C++ language standards are
being updated to address this with built-in atomic operations.

If you think you need to declare something “volatile”, that is a strong
indicator that you should be using one of the atomic operations instead.

Examples

In most cases you’d be better off with a synchronization primitive (like a
pthread mutex) rather than an atomic operation, but we will employ the latter to
illustrate how they would be used in a practical situation.

For the sake of brevity we’re ignoring the effects of compiler optimizations
here — some of this code is broken even on uniprocessors — so for
all of these examples you must assume that the compiler generates
straightforward code (for example, compiled with gcc -O0). The fixes presented here do
solve both compiler-reordering and memory-access-ordering issues, but we’re only
going to discuss the latter.

The idea here is that we allocate a structure, initialize its fields, and at
the very end we “publish” it by storing it in a global variable. At that point,
any other thread can see it, but that’s fine since it’s fully initialized,
right? At least, it would be on x86 SMP or a uniprocessor (again, making the
erroneous assumption that the compiler outputs code exactly as we have it in the
source).

Without a memory barrier, the store to gGlobalThing could be observed before
the fields are initialized on ARM. Another thread reading from thing->x could
see 5, 0, or even uninitialized data.

This can be fixed by changing the last assignment to:

atomic_release_store(&gGlobalThing, thing);

That ensures that all other threads will observe the writes in the proper
order, but what about reads? In this case we should be okay on ARM, because the
address dependency rules will ensure that any loads from an offset of
gGlobalThing are observed after the load of
gGlobalThing. However, it’s unwise to rely on architectural
details, since it means your code will be very subtly unportable. The complete
fix also requires a barrier after the load:

Now we know the ordering will be correct. This may seem like an awkward way
to write code, and it is, but that’s the price you pay for accessing data
structures from multiple threads without using locks. Besides, address
dependencies won’t always save us:

Because there is no relationship between the initialized field and the
others, the reads and writes can be observed out of order. (Note global data is
initialized to zero by the OS, so it shouldn’t be possible to read “random”
uninitialized data.)

We need to replace the store with:

atomic_release_store(&gGlobalThing.initialized, true);

and replace the load with:

int initialized = atomic_acquire_load(&gGlobalThing.initialized);

Another example of the same problem occurs when implementing
reference-counted data structures. The reference count itself will be
consistent so long as atomic increment and decrement operations are used, but
you can still run into trouble at the edges, for example:

The release() call decrements the reference count using a
barrier-free atomic decrement operation. Because this is an atomic RMW
operation, we know that it will work correctly. If the reference count goes to
zero, we recycle the storage.

The useSharedThing() function extracts what it needs from
sharedThing and then releases its copy. However, because we didn’t
use a memory barrier, and atomic and non-atomic operations can be reordered,
it’s possible for other threads to observe the read of
sharedThing->xafter they observe the recycle
operation. It’s therefore possible for localVar to hold a value
from "recycled" memory, for example a new object created in the same
location by another thread after release() is called.

This can be fixed by replacing the call to atomic_dec() with
atomic_release_dec(). The barrier ensures that the reads from
sharedThing are observed before we recycle the object.

In most cases the above won’t actually fail, because the “recycle” function
is likely guarded by functions that themselves employ barriers (libc heap
free()/delete(), or an object pool guarded by a
mutex). If the recycle function used a lock-free algorithm implemented without
barriers, however, the above code could fail on ARM SMP.

What not to do in Java

We haven’t discussed some relevant Java language features, so we’ll take a
quick look at those first.

Java's "synchronized" and "volatile" keywords

The “synchronized” keyword provides the Java language’s in-built locking
mechanism. Every object has an associated “monitor” that can be used to provide
mutually exclusive access.

The implementation of the “synchronized” block has the same basic structure
as the spin lock example: it begins with an acquiring CAS, and ends with a
releasing store. This means that compilers and code optimizers are free to
migrate code into a “synchronized” block. One practical consequence: you must
not conclude that code inside a synchronized block happens
after the stuff above it or before the stuff below it in a function. Going
further, if a method has two synchronized blocks that lock the same object, and
there are no operations in the intervening code that are observable by another
thread, the compiler may perform “lock coarsening” and combine them into a
single block.

The other relevant keyword is “volatile”. As defined in the specification
for Java 1.4 and earlier, a volatile declaration was about as weak as its C
counterpart. The spec for Java 1.5 was updated to provide stronger guarantees,
almost to the level of monitor synchronization.

The effects of volatile accesses can be illustrated with an example. If
thread 1 writes to a volatile field, and thread 2 subsequently reads from that
same field, then thread 2 is guaranteed to see that write and all writes
previously made by thread 1. More generally, the writes made by
any thread up to the point where it writes the field will be
visible to thead 2 when it does the read. In effect, writing to a volatile is
like a monitor release, and reading from a volatile is like a monitor
acquire.

Non-volatile accesses may be reorded with respect to volatile accesses in the
usual ways, for example the compiler could move a non-volatile load or store “above” a
volatile store, but couldn’t move it “below”. Volatile accesses may not be
reordered with respect to each other. The VM takes care of issuing the
appropriate memory barriers.

It should be mentioned that, while loads and stores of object references and
most primitive types are atomic, long and double
fields are not accessed atomically unless they are marked as volatile.
Multi-threaded updates to non-volatile 64-bit fields are problematic even on
uniprocessors.

Assume get() and incr() are called from multiple
threads, and we want to be sure that every thread sees the current count when
get() is called. The most glaring problem is that
mValue++ is actually three operations:

reg = mValue

reg = reg + 1

mValue = reg

If two threads execute in incr() simultaneously, one of the
updates could be lost. To make the increment atomic, we need to declare
incr() “synchronized”. With this change, the code will run
correctly in multi-threaded uniprocessor environments.

It’s still broken on SMP, however. Different threads might see different
results from get(), because we’re reading the value with an ordinary load. We
can correct the problem by declaring get() to be synchronized.
With this change, the code is obviously correct.

Unfortunately, we’ve introduced the possibility of lock contention, which
could hamper performance. Instead of declaring get() to be
synchronized, we could declare mValue with “volatile”. (Note
incr() must still use synchronize.) Now we know that
the volatile write to mValue will be visible to any subsequent volatile read of
mValue. incr() will be slightly slower, but
get() will be faster, so even in the absence of contention this is
a win if reads outnumber writes. (See also AtomicInteger.)

This has the same problem as the C code, namely that the assignment
sGoodies = goods might be observed before the initialization of the
fields in goods. If you declare sGoodies with the
volatile keyword, you can think about the loads as if they were
atomic_acquire_load() calls, and the stores as if they were
atomic_release_store() calls.

(Note that only the sGoodies reference itself is volatile. The
accesses to the fields inside it are not. The statement z =
sGoodies.x will perform a volatile load of MyClass.sGoodies
followed by a non-volatile load of sGoodies.x. If you make a local
reference MyGoodies localGoods = sGoodies, z =
localGoods.x will not perform any volatile loads.)

A more common idiom in Java programming is the infamous “double-checked
locking”:

The idea is that we want to have a single instance of a Helper
object associated with an instance of MyClass. We must only create
it once, so we create and return it through a dedicated getHelper()
function. To avoid a race in which two threads create the instance, we need to
synchronize the object creation. However, we don’t want to pay the overhead for
the “synchronized” block on every call, so we only do that part if
helper is currently null.

This doesn’t work correctly on uniprocessor systems, unless you’re using a
traditional Java source compiler and an interpreter-only VM. Once you add fancy
code optimizers and JIT compilers it breaks down. See the “‘Double Checked
Locking is Broken’ Declaration” link in the appendix for more details, or Item
71 (“Use lazy initialization judiciously”) in Josh Bloch’s Effective Java,
2nd Edition..

Running this on an SMP system introduces an additional way to fail. Consider
the same code rewritten slightly, as if it were compiled into a C-like language
(I’ve added a couple of integer fields to represent Helper’s
constructor activity):

Now the problem should be obvious: the store to helper is
happening before the memory barrier, which means another thread could observe
the non-null value of helper before the stores to the
x/y fields.

You could try to ensure that the store to helper happens after
the atomic_release_store() on this.lock by rearranging
the code, but that won’t help, because it’s okay to migrate code upward —
the compiler could move the assignment back above the
atomic_release_store() to its original position.

There are two ways to fix this:

Do the simple thing and delete the outer check. This ensures that we never
examine the value of helper outside a synchronized block.

Declare helper volatile. With this one small change, the code
in Example J-3 will work correctly on Java 1.5 and later. (You may want to take
a minute to convince yourself that this is true.)

This next example illustrates two important issues when using volatile:

Looking at useValues1(), if thread 2 hasn’t yet observed the
update to vol1, then it can’t know if data1 or
data2 has been set yet. Once it sees the update to
vol1, it knows that the change to data1 is also
visible, because that was made before vol1 was changed. However,
it can’t make any assumptions about data2, because that store was
performed after the volatile store.

The code in useValues2() uses a second volatile field,
vol2, in an attempt to force the VM to generate a memory barrier.
This doesn’t generally work. To establish a proper “happens-before”
relationship, both threads need to be interacting with the same volatile field.
You’d have to know that vol2 was set after data1/data2
in thread 1. (The fact that this doesn’t work is probably obvious from looking
at the code; the caution here is against trying to cleverly “cause” a memory
barrier instead of creating an ordered series of accesses.)

What to do

General advice

In C/C++, use the pthread operations, like mutexes and
semaphores. These include the proper memory barriers, providing correct and
efficient behavior on all Android platform versions. Be sure to use them
correctly, for example be wary of signaling a condition variable without holding the
corresponding mutex.

It's best to avoid using atomic functions directly. Locking and
unlocking a pthread mutex require a single atomic operation each if there’s no
contention, so you’re not going to save much by replacing mutex calls with
atomic ops. If you need a lock-free design, you must fully understand the
concepts in this entire document before you begin (or, better yet, find an
existing code library that is known to be correct on SMP ARM).

Be extremely circumspect with "volatile” in C/C++. It often indicates a
concurrency problem waiting to happen.

In Java, the best answer is usually to use an appropriate utility class from
the java.util.concurrent package. The code is well written and well
tested on SMP.

Perhaps the safest thing you can do is make your class immutable. Objects
from classes like String and Integer hold data that cannot be changed once the
class is created, avoiding all synchronization issues. The book Effective
Java, 2nd Ed. has specific instructions in “Item 15: Minimize Mutability”. Note in
particular the importance of declaring fields “final" (Bloch).

If neither of these options is viable, the Java “synchronized” statement
should be used to guard any field that can be accessed by more than one thread.
If mutexes won’t work for your situation, you should declare shared fields
“volatile”, but you must take great care to understand the interactions between
threads. The volatile declaration won’t save you from common concurrent
programming mistakes, but it will help you avoid the mysterious failures
associated with optimizing compilers and SMP mishaps.

The Java Memory Model guarantees that assignments to final fields are visible
to all threads once the constructor has finished — this is what ensures
proper synchronization of fields in immutable classes. This guarantee does not
hold if a partially-constructed object is allowed to become visible to other
threads. It is necessary to follow safe construction practices.(Safe
Construction Techniques in Java).

Synchronization primitive guarantees

The pthread library and VM make a couple of useful guarantees: all accesses
previously performed by a thread that creates a new thread are observable by
that new thread as soon as it starts, and all accesses performed by a thread
that is exiting are observable when a join() on that thread
returns. This means you don’t need any additional synchronization when
preparing data for a new thread or examining the results of a joined thread.

Whether or not these guarantees apply to interactions with pooled threads
depends on the thread pool implementation.

In C/C++, the pthread library guarantees that any accesses made by a thread
before it unlocks a mutex will be observable by another thread after it locks
that same mutex. It also guarantees that any accesses made before calling
signal() or broadcast() on a condition variable will
be observable by the woken thread.

Java language threads and monitors make similar guarantees for the comparable
operations.

Upcoming changes to C/C++

The C and C++ language standards are evolving to include a sophisticated
collection of atomic operations. A full matrix of calls for common data types
is defined, with selectable memory barrier semantics (choose from relaxed,
consume, acquire, release, acq_rel, seq_cst).

Closing Notes

While this document does more than merely scratch the surface, it doesn’t
manage more than a shallow gouge. This is a very broad and deep topic. Some
areas for further exploration:

Learn the definitions of happens-before,
synchronizes-with, and other essential concepts from the Java Memory
Model. (It’s hard to understand what “volatile” really means without getting
into this.)

Explore what compilers are and aren’t allowed to do when reordering code.
(The JSR-133 spec has some great examples of legal transformations that lead to
unexpected results.)

Find out how to write immutable classes in Java and C++. (There’s more to
it than just “don’t change anything after construction”.)

Internalize the recommendations in the Concurrency section of Effective
Java, 2nd Edition. (For example, you should avoid calling methods that are
meant to be overridden while inside a synchronized block.)

Understand what sorts of barriers you can use on x86 and ARM. (And other
CPUs for that matter, for example Itanium’s acquire/release instruction
modifiers.)

The Further Reading section in the appendix has links to
documents and web sites that will better illuminate these topics.

Appendix

SMP failure example

This document describes a lot of “weird” things that can, in theory, happen.
If you’re not convinced that these issues are real, a practical example may be
useful.

Bill Pugh’s Java memory model web site has a few
test programs on it. One interesting test is ReadAfterWrite.java, which does
the following:

Thread 1

Thread 2

for (int i = 0; i < ITERATIONS; i++) {
a = i;
BB[i] = b;
}

for (int i = 0; i < ITERATIONS; i++) {
b = i;
AA[i] = a;
}

Where a and b are declared as volatile
int fields, and AA and BB are ordinary
integer arrays.

This is trying to determine if the VM ensures that, after a value is written
to a volatile, the next read from that volatile sees the new value. The test
code executes these loops a million or so times, and then runs through afterward
and searches the results for inconsistencies.

At the end of execution,AA and BB will be full of
gradually-increasing integers. The threads will not run side-by-side in a
predictable way, but we can assert a relationship between the array contents.
For example, consider this execution fragment:

Thread 1

Thread 2

(initially a == 1534)
a = 1535
BB[1535] = 165
a = 1536
BB[1536] = 165

a = 1537
BB[1537] = 167

(initially b == 165)

b = 166
AA[166] = 1536
b = 167
AA[167] = 1536

(This is written as if the threads were taking turns executing so that it’s
more obvious when results from one thread should be visible to the other, but in
practice that won’t be the case.)

Look at the assignment of AA[166] in thread 2. We are capturing
the fact that, at the point where thread 2 was on iteration 166, it can see that
thread 1 was on iteration 1536. If we look one step in the future, at thread
1’s iteration 1537, we expect to see that thread 1 saw that thread 2 was at
iteration 166 (or later). BB[1537] holds 167, so it appears things
are working.

Now suppose we fail to observe a volatile write to b:

Thread 1

Thread 2

(initially a == 1534)
a = 1535
BB[1535] = 165
a = 1536
BB[1536] = 165

a = 1537
BB[1537] = 165 // stale b

(initially b == 165)

b = 166
AA[166] = 1536
b = 167
AA[167] = 1536

Now, BB[1537] holds 165, a smaller value than we expected, so we
know we have a problem. Put succinctly, for i=166, BB[AA[i]+1] < i. (This also
catches failures by thread 2 to observe writes to a, for example if we
miss an update and assign AA[166] = 1535, we will get
BB[AA[166]+1] == 165.)

If you run the test program under Dalvik (Android 3.0 “Honeycomb” or later)
on an SMP ARM device, it will never fail. If you remove the word “volatile”
from the declarations of a and b, it will consistently
fail. The program is testing to see if the VM is providing sequentially
consistent ordering for accesses to a and b, so you
will only see correct behavior when the variables are volatile. (It will also
succeed if you run the code on a uniprocessor device, or run it while something
else is using enough CPU that the kernel doesn’t schedule the test threads on
separate cores.)

If you run the modified test a few times you will note that it doesn’t fail
in the same place every time. The test fails consistently because it performs
the operations a million times, and it only needs to see out-of-order accesses
once. In practice, failures will be infrequent and difficult to locate. This
test program could very well succeed on a broken VM if things just happen to
work out.

Implementing synchronization stores

(This isn’t something most programmers will find themselves implementing,
but the discussion is illuminating.)

Consider once again volatile accesses in Java. Earlier we made reference to
their similarities with acquiring loads and releasing stores, which works as a
starting point but doesn’t tell the full story.

We start with a fragment of Dekker’s algorithm. Initially both
flag1 and flag2 are false:

Thread 1

Thread 2

flag1 = true
if (flag2 == false)critical-stuff

flag2 = true
if (flag1 == false)critical-stuff

flag1 and flag2 are declared as volatile boolean
fields. The rules for acquiring loads and releasing stores would allow the
accesses in each thread to be reordered, breaking the algorithm. Fortunately,
the JMM has a few things to say here. Informally:

A write to a volatile field happens-before every subsequent read of that
same field. (For this example, it means that if one thread updates a flag, and
later on the other thread reads that flag, the reader is guaranteed to see the
write.)

Every execution has a total order over all volatile field accesses. The
order is consistent with program order.

Taken together, these rules say that the volatile accesses in our example
must be observable in program order by all threads. Thus, we will never see
these threads executing the “critical-stuff” simultaneously.

Another way to think about this is in terms of data races. A data race
occurs if two accesses to the same memory location by different threads are not
ordered, at least one of them stores to the memory location, and at least one of
them is not a synchronization action (Boehm and McKenney). The memory model
declares that a program free of data races must behave as if executed by a
sequentially-consistent machine. Because both flag1 and
flag2 are volatile, and volatile accesses are considered
synchronization actions, there are no data races and this code must execute in a
sequentially consistent manner.

As we saw in an earlier section, we need to insert a store/load barrier
between the two operations. The code executed in the VM for a volatile access
will look something like this:

volatile load

volatile store

reg = Aload/load + load/store barrier

store/store barrier
A = regstore/load barrier

The volatile load is just an acquiring load. The volatile store is similar
to a releasing store, but we’ve omitted load/store from the pre-store barrier,
and added a store/load barrier afterward.

What we’re really trying to guarantee, though, is that (using thread 1 as an
example) the write to flag1 is observed before the read of flag2. We could
issue the store/load barrier before the volatile load instead and get the same
result, but because loads tend to outnumber stores it’s best to associate it
with the store.

On some architectures, it’s possible to implement volatile stores with an
atomic operation and skip the explicit store/load barrier. On x86, for example,
atomics provide a full barrier. The ARM LL/SC operations don’t include a
barrier, so for ARM we must use explicit barriers.

(Much of this is due to Doug Lea and his “JSR-133 Cookbook for Compiler
Writers” page.)

Further reading

Web pages and documents that provide greater depth or breadth. The more generally useful articles are nearer the top of the list.

The documentation for the java.util.concurrent package. Near the bottom of the page is a section entitled “Memory Consistency Properties” that explains the guarantees made by the various classes.
java.util.concurrent Package Summary

The “first known correct solution to the mutual exclusion problem in concurrent programming”. The wikipedia article has the full algorithm, with a discussion about how it would need to be updated to work with modern optimizing compilers and SMP hardware.
http://en.wikipedia.org/wiki/Dekker's_algorithm

Doug Lea wrote this as a companion to the JSR-133 (Java Memory Model) documentation. It goes much deeper into the details than most people will need to worry about, but it provides good fodder for contemplation.
http://g.oswego.edu/dl/jmm/cookbook.html